Image enhancement and surface roughness with feature extraction using DWT

Design Implementation and Hardware Structure for Image Enhancement and Surface Roughness With Feature Extraction Using Discrete Wavelet Transform

“…Human vision can be replaced with machine vision with capturing, compressing and extraction of image in high speed precision manufacturing areas as a mainstream automation tool (Badashah and Subbaiah, 2011). Machine vision (Luk et al, 1989;Al-Kindi et al, 1992) has the advantage of grasping the images online without accounting for factors like vibrations (advantage), noise, intensity (disadvantage) (Tsai and Tseng, 1999).…”

A Selective Multiclass Support Vector Machine Ensemble Classifier for Engineering Surface Classification Using High Definition Metrology

“…In this study, attention is mainly focused on wavelet filters, due to the fact that it can provide multiscale/orientation analysis, which makes it a powerful tool in fea ture extraction and is superior to traditional filters [16,18]. There are many kinds of wavelets, and discrete wavelet transform (DWT) is the most widely used one in the analysis of workpiece surfaces in previous studies [16,19,20]. However, DWT mainly has two disadvantages: lack of shift-invariance and poor direc tional selectivity for diagonal features, which impair its applica tion in engineering surface analysis.…”